Using data mining tools to investigate patterns in complex data sets Preprocessing data for data mining (e.g., transforming numeric values to nominal values, discretizing data) Using decision tree classifiers to investigate classification and regression problems Applying cross-validation methods Interpreting and drawing inferences from the results of data mining Assessing the predictive performance of classifiers by examining key error metrics Identifying where learning methods fail and gain insight into why with error analysis Drawing relationships between learner performance and measured features to help understand model performance Conducting feature selection to investigate the correlation between different features in a dataset Presenting data mining results to management